To understand how motion can help improve the performance of attention-based methods we perform ablation studies across three CNN backbones including 2D-ResNet [1]}, 2D-MobileNet[2]}, and I3D-inflated-ResNet [3]}. Furthermore, we use Grad-CAM [4]} to visualize how the model’s focus changes when using motion, attention, and both motion and attention. We also compare the performance of M2A to other state-of-the-art (SOTA) mechanisms on the video action recognition benchmark SomethingSomething V1 [5]}. Finally, to understand if further performance improvements can be achieved, we investigate incorporating M2A with other temporal mechanisms and the change in performance when state-of-the-art motion-only and attention-only mechanisms are combined.
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